# Neural Network Questions and Answers – Pattern Classification – 1

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This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Pattern Classification – 1″.

1. What is the objective of perceptron learning?
a) class identification
c) adjust weight along with class identification
d) none of the mentioned

Explanation: The objective of perceptron learning is to adjust weight along with class identification.

2. On what factor the number of outputs depends?
a) distinct inputs
b) distinct classes
c) both on distinct classes & inputs
d) none of the mentioned

Explanation: Number of outputs depends on number of classes.

3. In perceptron learning, what happens when input vector is correctly classified?
a) small adjustments in weight is done
b) large adjustments in weight is done
c) no adjustments in weight is done
d) weight adjustments doesn’t depend on classification of input vector

Explanation: No adjustments in weight is done, since input has been correctly classified which is the objective of the system.
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4. When two classes can be separated by a separate line, they are known as?
a) linearly separable
b) linearly inseparable classes
c) may be separable or inseparable, it depends on system
d) none of the mentioned

Explanation: Linearly separable classes, functions can be separated by a line.

5. If two classes are linearly inseparable, can perceptron convergence theorem be applied?
a) yes
b) no

Explanation: Perceptron convergence theorem can only be applied, if and only if two classses are linearly separable.

6. Two classes are said to be inseparable when?
a) there may exist straight lines that doesn’t touch each other
b) there may exist straight lines that can touch each other
c) there is only one straight line that separates them
d) all of the mentioned

Explanation: Linearly separable classes, functions can be separated by a line.

7. Is it necessary to set initial weights in prceptron convergence theorem to zero?
a) yes
b) no

Explanation: Initial setting of weights doesn’t affect perceptron convergence theorem.

8. The perceptron convergence theorem is applicable for what kind of data?
a) binary
b) bipolar
c) both binary and bipolar
d) none of the mentioned

Explanation: The perceptron convergence theorem is applicable for both binary and bipolar input, output data.

9. w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?
a) yes
b) no

Explanation: Gradient descent can be used as perceptron learning.

10. If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight
a) e(m) = n(b(m) – s(m)) a(m)
b) e(m) = n(b(m) – s(m))
c) e(m) = (b(m) – s(m))
d) none of the mentioned

Explanation: Error is difference between desired and actual output.

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